面向产品评论的细粒度情感分析

针对传统粗粒度情感分析忽略具体评价对象,以及现有细粒度情感分析方法忽略无关评价要素的问题,提出结合条件随机场(CRF)和语法树剪枝的方法对产品评论进行细粒度情感分析。采用基于MapReduce的并行化协同训练(Tri-training)的方法对语料进行半自主标注,利用融合多种语言特征的条件随机场模型,获取评论中的评价对象和正负面评价词。通过建立领域本体和句法路径库实现语法树剪枝,对含有多个评价对象和评价词的文本,去掉无关评价对象的干扰,抽取出正确的评价单元,最后形成可视化产品报告。实验结果显示,提出的方法在两种不同领域数据集上,识别情感要素的综合准确率达89%左右,情感评价单元的综合准确率也达89%左右。实验结果表明,与传统方法相比,结合CRF和语法树剪枝的方法识别准确率更高,性能更好。

Fine-grained sentiment analysis oriented to product comment

The traditional sentiment analysis is coarse-grained and ignores the comment targets, the existing fine-grained sentiment analysis ignores multi-target and multi-opinion sentences. In order to solve these problems, a method of fine-grained sentiment analysis based on Conditional Random Field (CRF) and syntax tree pruning was proposed. A parallel tri-training method based on MapReduce was used to label corpus autonomously. CRF model of integrating various features was used to extract positive/negative opinions and the target of opinions from comment sentences. To deal with the multi-target and multi-opinion sentences, syntax tree pruning was employed through building domain ontology and syntactic path library to eliminate the irrelevant target of opinions and extract the correct appraisal expressions. Finally, a visual product attribute report was generated. After syntax tree pruning, the accuracy of the proposed method on sentiment elements and appraisal expression can reach 89% approximately.The experimental results on two product domains of mobile phone and camera show that the proposed method outperforms the traditional methods on both sentiment analysis accuracy and training performance.